Electrical energy providers, including utility companies are often in a situation where it would be advantageous to temporarily reduce demand for electrical power (“kW demand”) by customers (“end users”). In particular, in times of peak demand it is advantageous to reduce overall energy consumption and therefore reduce the burden on the electrical power generators that provide power to the electrical power grid. When overall energy consumption is sufficiently reduced during times of peak demand, the electrical power grid may be stabilized such that electrical power may be reliably supplied to end users, thereby avoiding brownouts or possibly blackouts.
In order to limit energy consumption during times of peak demand, electrical utilities have traditionally increased the price for electricity during the times when it is known that electrical energy demand will be high. The hope is that the increased price for electricity during these times of high demand will cause end users to limit electrical energy consumption, and therefore avoid overloading the electrical power grid during the times of peak demand. However, electrical utilities have discovered that merely raising the price of electricity during the times of high demand is often insufficient to avoid excessive demand. Therefore, additional systems and initiatives have been developed to encourage end users to shed electrical loads during times of high demand.
Demand Response (“DR”) agreements have been used by energy providers to request electrical load shedding. With DR agreements, the electric energy provider contacts certain end users during DR events that are associated with times of peak demand. In exchange for load shedding during these DR events, the end user is given certain price reductions or rebates. The DR agreements benefit the electric energy provider by reduced energy consumption during times of high demand, and also benefit the end user through energy price reductions or rebates.
Communications from the electric energy provider to the end user indicating that a DR event would occur in the near future were initially in the form of telephone calls or emails. After receiving such telephone calls or emails, the end user would take the appropriate action to reduce energy consumption under the DR agreement. For example, during a DR event in hot weather, a building operator may temporarily increase the thermostat temperature, dim the lights, increase refrigerator temperature, or take other action to reduce energy consumption during the DR event. This action typically occurred manually by an individual making the appropriate adjustments to various building control systems.
With more modern systems, DR events are typically communicated to the end user automatically over a network by computers using a client-server model. In particular, a DR server at the electric energy provider may communicate DR events to a DR client at the premises of the end user. The DR server may push data concerning the Demand Response event to the DR client, or the DR client may poll the DR server for data concerning DR events. Various protocols exist for communicating DR signals between the DR server and the DR client. One such protocol is the OpenADR (Open Automated Demand Response Communication Specification Version 1.0) protocol developed by Lawrence Berkeley National Lab and Akuacom. OpenADR has been adopted by California utilities and could become a national standard for communicating demand response signals. Under current demand response systems, when a DR client receives a DR event message providing information concerning a DR event from a DR server, the DR event message is passed on to an individual or system responsible to take corresponding load shedding actions.
While past demand response systems have been helpful in reducing energy consumption during periods of high demand, it would be advantageous to improve upon these systems. In particular, it would be advantageous to provide a Demand Response approach that is automated and efficiently reduces electrical energy consumption in a facility during various DR events based on user configured strategies with load shedding logic when responding to the DR messages.